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Research map / Architectures and efficiency / RNNs and normalization

Regularizing and Optimizing LSTM Language Models

ICLR 2018 · Architectures and efficiency › RNNs and normalization · approach: LSTM design and memory · 454 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add a continuous cache pointerAWD-LSTM (tied) · Penn Treebank testperplexity: 57.3 -> 52.8Helped
Add a continuous cache pointerAWD-LSTM (tied) · WikiText-2 testperplexity: 65.8 -> 52.0Helped
Remove fine-tuning (ablation)AWD-LSTM (tied) · Penn Treebank testperplexity: 57.3 -> 58.8Hurt
Remove fine-tuning (ablation)AWD-LSTM (tied) · WikiText-2 testperplexity: 65.8 -> 66.0Hurt
Remove NT-ASGD (ablation)AWD-LSTM (tied) · Penn Treebank testperplexity: 57.3 -> 63.7Hurt
Remove NT-ASGD (ablation)AWD-LSTM (tied) · WikiText-2 testperplexity: 65.8 -> 69.7Hurt
Use fixed rather than variable sequence lengths (ablation)AWD-LSTM (tied) · Penn Treebank testperplexity: 57.3 -> 58.9Hurt
Remove embedding dropout (ablation)AWD-LSTM (tied) · Penn Treebank testperplexity: 57.3 -> 62.7Hurt
Remove weight decay (ablation)AWD-LSTM (tied) · Penn Treebank testperplexity: 57.3 -> 61.0Hurt
Remove AR/TAR (ablation)AWD-LSTM (tied) · WikiText-2 testperplexity: 65.8 -> 70.1Hurt
Use full-sized embedding (ablation)AWD-LSTM (tied) · Penn Treebank testperplexity: 57.3 -> 65.6Hurt
Remove weight-dropping (ablation)AWD-LSTM (tied) · Penn Treebank testperplexity: 57.3 -> 68.9Hurt

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